{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temporal Scoring Filter (Altdorff and Schrön, 2023)\n",
    "\n",
    "This Notebook contains an example application of the temporal scoring filter introduced by (Altdorff and Schrön, 2023).\n",
    "\n",
    "It makes use of the data file `santa_rita_short.csv` which includes:\n",
    "- `SWC_Ref`: references soil moisture from weight-averaged TDR sensors,\n",
    "- `SWC`: noisy soil moisture data from CRNS (to be filtered),\n",
    "- `rain`: rain data of the Santa Rita site.\n",
    "\n",
    "We thankfully acknowledge Trenton Franz for sharing the data ([Franz et al., 2012](https://doi.org/10.2136/vzj2012.0046)).\n",
    "\n",
    "Contact:\n",
    "- daniel.altdorff@uni-potsdam.de\n",
    "- martin.schroen@ufz.de"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "nteract": {
     "transient": {
      "deleting": false
     }
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    "tags": []
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   "source": [
    "import pandas\n",
    "import numpy as np\n",
    "# Will be imported later when they are needed:\n",
    "# - warnings\n",
    "# - sklearn.metrics\n",
    "# - matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define metrics functions for R² and RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define metrics functions\n",
    "from sklearn.metrics import r2_score as R2, mean_squared_error as MSE\n",
    "\n",
    "def get_R2(data, column1: str, column2: str) -> float:\n",
    "    \"\"\"\n",
    "    Takes a pandas dataframe and two column names.\n",
    "    Calculates the R² value between those columns.\n",
    "    Returns R² with 4 digits.\n",
    "    Usage:\n",
    "        r2 = get_R2(data, 'SWC_Ref', 'SWC')\n",
    "        print('R² = %.3f' % r2)\n",
    "    \"\"\"\n",
    "    d = data[[column1, column2]].dropna().copy()\n",
    "    v = R2(d[column1], d[column2])\n",
    "    return(round(v, 4))\n",
    "\n",
    "def get_RMSE(data, column1: str, column2: str) -> float:\n",
    "    \"\"\"\n",
    "    Takes a pandas dataframe and two column names.\n",
    "    Calculates the RMSE value between those columns.\n",
    "    Returns RMSE with 4 digits.\n",
    "    Usage:\n",
    "        rmse = get_RMSE(data, 'SWC_Ref', 'SWC')\n",
    "        print('RMSE = %.3f m³/m³' % rmse)\n",
    "    \"\"\"\n",
    "    d = data[[column1, column2]].dropna().copy()\n",
    "    v = MSE(d[column1], d[column2], squared=False)\n",
    "    return(round(v, 4))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the temporal scoring filter (SF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Temporal scoring\n",
    "def scoring(\n",
    "    data,                     # Pandas DataFrame\n",
    "    column: str = 'SWC',      # Variable column to work on\n",
    "    scope: int = 48,          # Scope window before and after each data point\n",
    "    threshold: int = 2,       # Threshold score below which values are chosen\n",
    "    stdev_factor: float = 1.5 # Factor on the std.dev.\n",
    "    ):\n",
    "    \"\"\"\n",
    "    Takes a DataFrame, a column, and some parameters,\n",
    "    Calculates the score of each data point in that column,\n",
    "    Returns the score and selected data points below a score threshold.\n",
    "    Usage:\n",
    "        data_scored = scoring(data, column=\"SWC\", scope=48, threshold=2)\n",
    "    \"\"\"\n",
    "\n",
    "    # Suppress fragmentation warnings\n",
    "    from warnings import simplefilter\n",
    "    simplefilter(action=\"ignore\", category=pandas.errors.PerformanceWarning)\n",
    "    \n",
    "    # Shortcuts\n",
    "    S = column\n",
    "    D = data[[S]].copy()\n",
    "    \n",
    "    # Create row shifts across the provided scope\n",
    "    # Example for scope = 1:\n",
    "    #       var var-1 var+1\n",
    "    #     1  42   NaN    23\n",
    "    #     2  23    42    19\n",
    "    #     3  19    23   NaN\n",
    "    #     4        19   NaN\n",
    "    shifts = np.concatenate((np.arange(-scope,0), np.arange(1,scope+1)))\n",
    "    for s in shifts:\n",
    "        D[S + '%+d' % s] = D[S].shift(s)\n",
    "\n",
    "    # Prepare queries for selecting data before or after the main point\n",
    "    scope_all = [S+'%+d' % s for s in shifts]\n",
    "    scope_bef = [S+'%+d' % s for s in np.arange(-scope, 0)]\n",
    "    scope_aft = [S+'%+d' % s for s in np.arange(1,scope+1)]\n",
    "    \n",
    "    # Set new columns to find the mean\n",
    "    D['mean+-'] =  D.loc[:, scope_all].mean(axis=1)\n",
    "    D['mean-' ] =  D.loc[:, scope_bef].mean(axis=1)\n",
    "    D['mean+' ] =  D.loc[:, scope_aft].mean(axis=1)\n",
    "\n",
    "    # stdev and median\n",
    "    D['stdev+-']  = (D.loc[:, scope_all].std(axis = 1) *stdev_factor)**2\n",
    "    D['median+-'] =  D.loc[:, scope_all].median(axis = 1)\n",
    "\n",
    "    # Apply the different scoring queries\n",
    "    # 1\n",
    "    D['minus_mean']      = D.loc[:,S] - D.loc[:,'mean+-'] >0\n",
    "    # 2\n",
    "    D['minus_median']    = D.loc[:,S] - D.loc[:,'median+-'] >0\n",
    "    # 3\n",
    "    D['S_follow_SD']     = ((D.loc[:,S]) - (D.loc[:,S+'+1']))**2  > D.loc[:,'stdev+-']\n",
    "    # 4\n",
    "    D['S_before_SD']     = ((D.loc[:,S]) - (D.loc[:,S+'-1']))**2  > D.loc[:,'stdev+-']\n",
    "    # 5\n",
    "    D['S_higher_follow'] = ((D.loc[:,S]) - (D.loc[:,S+'-1']))  > 0\n",
    "    # 6\n",
    "    D['S_higher_before'] = ((D.loc[:,S]) - (D.loc[:,S+'+1']))  > 0\n",
    "    # 6.5\n",
    "    D['S_Paramount']     = D['S_higher_follow'] & D['S_higher_before'] \n",
    "    # 7\n",
    "    D['S_minus_before']  = ((D.loc[:,S]) - (D.loc[:,'mean+']))**2  > D.loc[:,'stdev+-']\n",
    "    # 8\n",
    "    D['S_minus_follow']  = ((D.loc[:,S]) - (D.loc[:,'mean-']))**2  > D.loc[:,'stdev+-']\n",
    "    # 9\n",
    "    D['S_minus_mean']    = ((D.loc[:,S]) - (D.loc[:,'mean+-']))**2 > D.loc[:,'stdev+-']\n",
    "\n",
    "    # Take the sum of all scores\n",
    "    D['score'] = D[[\n",
    "        'minus_mean',\n",
    "        'minus_median',\n",
    "        'S_follow_SD',\n",
    "        'S_before_SD',\n",
    "        'S_minus_before',\n",
    "        'S_minus_follow',\n",
    "        'S_minus_mean',\n",
    "        'S_Paramount'\n",
    "    ]].sum(axis=1)\n",
    "\n",
    "    # Select only variables below a threshold score\n",
    "    D[S+'_chosen'] = D.loc[ D['score'] < threshold, S]\n",
    "\n",
    "    # return DataFrame with the original variable, the chosen values, and the score\n",
    "    return D[[S, S+'_chosen', \"score\"]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Data input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data\n",
    "data = pandas.read_csv(\n",
    "    'santa_rita_short.csv',\n",
    "    index_col = 0,\n",
    "    parse_dates = True)\n",
    "\n",
    "# Remove faulty data\n",
    "data.loc[data[\"SWC\"]<0.001, \"SWC\"] = np.nan\n",
    "\n",
    "# First glimpse\n",
    "_ = data.plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application of the scoring filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply scoring\n",
    "data_scored = scoring(data, column=\"SWC\", scope=48, threshold=2)\n",
    "\n",
    "# Plot\n",
    "_ = data_scored[[\"SWC\",\"SWC_chosen\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join columns into original data frame\n",
    "data[\"SWC_chosen\"] = data_scored[\"SWC_chosen\"]\n",
    "data[\"score\"]      = data_scored[\"score\"]\n",
    "\n",
    "# Create a daily aggregation\n",
    "data_daily = data.resample('1D').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the performance against the TDR reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 SWC: R² = 0.725, RMSE = 0.013 m³/m³\n",
      "          SWC_chosen: R² = 0.886, RMSE = 0.009 m³/m³\n"
     ]
    }
   ],
   "source": [
    "for var in [\"SWC\", \"SWC_chosen\"]:\n",
    "    r2   = get_R2(  data_daily, 'SWC_Ref', var)\n",
    "    rmse = get_RMSE(data_daily, 'SWC_Ref', var)\n",
    "    print('%20s: R² = %.3f, RMSE = %.3f m³/m³' % (var, r2, rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create first axis\n",
    "fig, ax1 = plt.subplots(figsize = (9, 6))\n",
    "\n",
    "# Plot data: CRNS conventional, CRNS score filtered, TDR\n",
    "ax1.plot(\n",
    "    data_daily['SWC'],\n",
    "    color=\"#CCCCCC\",\n",
    "    label=\"CRNS conventional\"\n",
    ")\n",
    "ax1.plot(\n",
    "    data_daily['SWC_chosen'],\n",
    "    color=\"C2\",\n",
    "    label=\"CRNS score filtered\"\n",
    ")\n",
    "ax1.plot(\n",
    "    data_daily['SWC_Ref'],\n",
    "    color=\"k\",\n",
    "    label=\"TDR reference measurements\"\n",
    ")\n",
    "ax1.set_ylim(0,0.3)\n",
    "ax1.set_ylabel(\"Soil moisture (m³/m³)\")\n",
    "ax1.legend(ncol=3, frameon=False)\n",
    "\n",
    "# Copy axis to plot the rain \n",
    "ax2 = ax1.twinx()\n",
    "ax2.bar(data_daily.index, data_daily['rain'].values, color='C0')\n",
    "ax2.set_ylabel(\"Rain (mm)\")\n",
    "ax2.set_ylim(0,4)\n",
    "\n",
    "plt.show() "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
